mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914461523116032 |
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| author | Vergani, Andrea Mario Iapaolo, Valeria Di Angelantonio, Emanuele Masseroli, Marco Ieva, Francesca |
| author_facet | Vergani, Andrea Mario Iapaolo, Valeria Di Angelantonio, Emanuele Masseroli, Marco Ieva, Francesca |
| contents | mmid (Multi-Modal Integration and Downstream analyses for healthcare analytics) is a Python package that offers multi-modal fusion and imputation, classification, time-to-event prediction and clustering functionalities under a single interface, filling the gap of sequential data integration and downstream analyses for healthcare applications in a structured and flexible environment. mmid wraps in a unique package several algorithms for multi-modal decomposition, prediction and clustering, which can be combined smoothly with a single command and proper configuration files, thus facilitating reproducibility and transferability of studies involving heterogeneous health data sources. A showcase on personalised cardiovascular risk prediction is used to highlight the relevance of a composite pipeline enabling proper treatment and analysis of complex multi-modal data. We thus employed mmid in an example real application scenario involving cardiac magnetic resonance imaging, electrocardiogram, and polygenic risk scores data from the UK Biobank. We proved that the three modalities captured joint and individual information that was used to (1) early identify cardiovascular disease before clinical manifestations with cardiological relevance, and (2) do it better than single data sources alone. Moreover, mmid allowed to impute partially observable data modalities without considerable performance losses in downstream disease prediction, thus proving its relevance for real-world health analytics applications (which are often characterised by the presence of missing data). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08334 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python Vergani, Andrea Mario Iapaolo, Valeria Di Angelantonio, Emanuele Masseroli, Marco Ieva, Francesca Computation Applications mmid (Multi-Modal Integration and Downstream analyses for healthcare analytics) is a Python package that offers multi-modal fusion and imputation, classification, time-to-event prediction and clustering functionalities under a single interface, filling the gap of sequential data integration and downstream analyses for healthcare applications in a structured and flexible environment. mmid wraps in a unique package several algorithms for multi-modal decomposition, prediction and clustering, which can be combined smoothly with a single command and proper configuration files, thus facilitating reproducibility and transferability of studies involving heterogeneous health data sources. A showcase on personalised cardiovascular risk prediction is used to highlight the relevance of a composite pipeline enabling proper treatment and analysis of complex multi-modal data. We thus employed mmid in an example real application scenario involving cardiac magnetic resonance imaging, electrocardiogram, and polygenic risk scores data from the UK Biobank. We proved that the three modalities captured joint and individual information that was used to (1) early identify cardiovascular disease before clinical manifestations with cardiological relevance, and (2) do it better than single data sources alone. Moreover, mmid allowed to impute partially observable data modalities without considerable performance losses in downstream disease prediction, thus proving its relevance for real-world health analytics applications (which are often characterised by the presence of missing data). |
| title | mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python |
| topic | Computation Applications |
| url | https://arxiv.org/abs/2604.08334 |